A Semi-Automated Approach for Incremental Migration from Monolithic to Microservices Architecture
Bibliographic record
Abstract
As software applications grow in size and complexity, maintaining and scaling them becomes increasingly difficult. Traditional monolithic architectures, where all components are combined into a single unit, often face issues such as limited scalability, cumbersome maintenance, and problematic deployment. The microservices architecture has emerged as a solution, breaking down applications into smaller, loosely coupled services, each responsible for a specific business function. This transition process, known as migration, is challenging due to the difficulty in determining the optimal decomposition of the monolithic system. This thesis presents a novel framework designed to facilitate the migration from monolithic to microservices architecture using the Strangler Fig Pattern. Unlike existing approaches that typically attempt to decompose the monolith in a single iteration, our framework supports a gradual, iterative migration process. This allows for smoother transitions, reduced risk, and better management of complexity. Key contributions of this work include the development of a tool that leverages both static and dynamic analysis to identify microservice candidates. The tool integrates these heterogeneous data sources using the Single Source of Truth (SST) paradigm, ensuring consistency and reliability. The performance of the tool is evaluated on two well-known Java Spring projects, demonstrating its effectiveness in creating well-modularized, cohesive, and loosely coupled microservices. The results show that our approach not only meets the desired principles of microservice architectures but also compares favorably with other state-of-the-art methods. By providing a practical and systematic solution for gradual migration, this thesis addresses a significant gap in the existing literature and offers valuable insights for practitioners seeking to modernize large-scale software systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".